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Update app.py
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app.py
CHANGED
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@@ -6,22 +6,20 @@ import docx2txt
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from docx import Document
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from fpdf import FPDF
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from langdetect import detect
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# ===
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os.makedirs("/app/models", exist_ok=True)
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FONT_PATH = "DejaVuSans.ttf"
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# === 2. Проверяем, есть ли шрифт, если нет — скачиваем ===
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if not os.path.exists(FONT_PATH):
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import urllib.request
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print("🪶 Загружаю шрифт DejaVuSans.ttf...")
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urllib.request.urlretrieve(
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"https://github.com/dejavu-fonts/dejavu-fonts/raw/master/ttf/DejaVuSans.ttf",
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FONT_PATH
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)
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# ===
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def load_model(task, model_name):
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return pipeline(task, model=model_name, cache_dir="/app/models")
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summarizers = {
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@@ -30,7 +28,7 @@ summarizers = {
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"kz": load_model("summarization", "csebuetnlp/mT5_multilingual_XLSum")
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}
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# ===
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def read_file(file):
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if not file:
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return ""
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@@ -48,10 +46,10 @@ def read_file(file):
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else:
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text = file.read().decode("utf-8", errors="ignore")
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except Exception as e:
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return f"Ошибка при чтении файла: {e}"
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return text.strip()
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# ===
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def detect_language(text):
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try:
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lang = detect(text)
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@@ -64,7 +62,7 @@ def detect_language(text):
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except:
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return "en"
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# ===
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def summarize_text(text):
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if not text or len(text) < 50:
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return "⚠️ Недостаточно текста для анализа.", "❌", "❌", 0, 0, "❌"
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@@ -73,8 +71,8 @@ def summarize_text(text):
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model = summarizers.get(lang, summarizers["en"])
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flags = {"ru": "🇷🇺 Русский", "kz": "🇰🇿 Қазақ тілі", "en": "🇬🇧 English"}
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lang_label = flags.get(lang, "🌍 Unknown")
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model_label = model.model.name_or_path if hasattr(model.model, "name_or_path") else "Custom"
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chunk_size = 2500
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overlap = 200
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summaries = []
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@@ -94,7 +92,7 @@ def summarize_text(text):
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return summary, lang_label, model_label, src_len, sum_len, f"{compression}%"
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# ===
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def save_summary_as_txt(summary_text):
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path = "summary.txt"
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with open(path, "w", encoding="utf-8") as f:
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@@ -113,45 +111,48 @@ def save_summary_as_pdf(summary_text):
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path = "summary.pdf"
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pdf = FPDF()
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pdf.add_page()
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pdf.set_font('DejaVu', '', 12)
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except:
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pdf.set_font("Arial", size=12)
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pdf.multi_cell(0, 10, summary_text)
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pdf.output(path)
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return path
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# ===
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def summarize_file(file):
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text = read_file(file)
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if text.startswith("
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return text, "❌", "❌", 0, 0, "❌", None, None, None
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txt_path = save_summary_as_txt(summary)
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docx_path = save_summary_as_docx(summary)
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pdf_path = save_summary_as_pdf(summary)
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return summary, lang_label, model_label, src_len, sum_len, compression, txt_path, docx_path, pdf_path
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# ===
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demo = gr.Interface(
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fn=summarize_file,
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inputs=gr.File(label="
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outputs=[
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gr.Textbox(label="🧾 Краткое резюме"),
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gr.Textbox(label="🌍 Определённый язык"),
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gr.Textbox(label="🧠 Используемая модель"),
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gr.Number(label="📄 Длина исходного текста"),
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gr.Number(label="📝 Длина резюме"),
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gr.Textbox(label="📉
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gr.File(label="
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gr.File(label="📘
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gr.File(label="📕
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],
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title="🧠 Eroha Summarizer PRO (автономная версия)",
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description="🚀
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)
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demo.launch(server_name="0.0.0.0", server_port=7860)
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from docx import Document
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from fpdf import FPDF
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from langdetect import detect
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import urllib.request
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# === Папки и шрифт ===
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os.makedirs("/app/models", exist_ok=True)
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FONT_PATH = "DejaVuSans.ttf"
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if not os.path.exists(FONT_PATH):
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urllib.request.urlretrieve(
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"https://github.com/dejavu-fonts/dejavu-fonts/raw/master/ttf/DejaVuSans.ttf",
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FONT_PATH
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)
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# === Загрузка моделей ===
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def load_model(task, model_name):
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print(f"🔹 Загружается модель: {model_name}")
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return pipeline(task, model=model_name, cache_dir="/app/models")
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summarizers = {
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"kz": load_model("summarization", "csebuetnlp/mT5_multilingual_XLSum")
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}
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# === Чтение файлов ===
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def read_file(file):
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if not file:
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return ""
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else:
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text = file.read().decode("utf-8", errors="ignore")
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except Exception as e:
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return f"⚠️ Ошибка при чтении файла: {e}"
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return text.strip()
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# === Определение языка ===
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def detect_language(text):
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try:
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lang = detect(text)
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except:
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return "en"
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# === Суммаризация ===
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def summarize_text(text):
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if not text or len(text) < 50:
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return "⚠️ Недостаточно текста для анализа.", "❌", "❌", 0, 0, "❌"
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model = summarizers.get(lang, summarizers["en"])
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flags = {"ru": "🇷🇺 Русский", "kz": "🇰🇿 Қазақ тілі", "en": "🇬🇧 English"}
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lang_label = flags.get(lang, "🌍 Unknown")
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model_label = model.model.name_or_path if hasattr(model.model, "name_or_path") else "Custom"
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chunk_size = 2500
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overlap = 200
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summaries = []
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return summary, lang_label, model_label, src_len, sum_len, f"{compression}%"
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# === Сохранение результатов ===
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def save_summary_as_txt(summary_text):
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path = "summary.txt"
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with open(path, "w", encoding="utf-8") as f:
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path = "summary.pdf"
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pdf = FPDF()
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pdf.add_page()
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pdf.add_font('DejaVu', '', FONT_PATH, uni=True)
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pdf.set_font('DejaVu', '', 12)
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pdf.multi_cell(0, 10, summary_text)
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pdf.output(path)
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return path
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# === Главная функция ===
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def summarize_file(file):
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text = read_file(file)
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if text.startswith("⚠️"):
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return text, "❌", "❌", 0, 0, "❌", None, None, None
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with gr.Progress(track_tqdm=True) as progress:
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progress(0, desc="🧠 Анализ текста...")
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summary, lang_label, model_label, src_len, sum_len, compression = summarize_text(text)
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progress(1, desc="✅ Готово!")
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txt_path = save_summary_as_txt(summary)
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docx_path = save_summary_as_docx(summary)
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pdf_path = save_summary_as_pdf(summary)
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return summary, lang_label, model_label, src_len, sum_len, compression, txt_path, docx_path, pdf_path
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# === Интерфейс ===
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demo = gr.Interface(
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fn=summarize_file,
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inputs=gr.File(label="📂 Загрузите документ (.pdf, .docx, .txt)"),
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outputs=[
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gr.Textbox(label="🧾 Краткое резюме"),
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gr.Textbox(label="🌍 Определённый язык"),
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gr.Textbox(label="🧠 Используемая модель"),
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gr.Number(label="📄 Длина исходного текста"),
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gr.Number(label="📝 Длина резюме"),
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gr.Textbox(label="📉 Сжатие"),
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gr.File(label="�� TXT"),
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gr.File(label="📘 DOCX"),
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gr.File(label="📕 PDF"),
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],
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title="🧠 Eroha Summarizer PRO (автономная версия)",
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description="🚀 Определяет язык (🇷🇺 / 🇰🇿 / 🇬🇧), создаёт краткое резюме и сохраняет в TXT, DOCX, PDF.",
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theme="soft",
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allow_flagging="never"
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)
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demo.launch(server_name="0.0.0.0", server_port=7860)
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